Multi-trophic level response to extreme metal contamination from gold mining in a subarctic lake
Bibliographic record
Abstract
Giant Mine, located in the city of Yellowknife (Northwest Territories, Canada), is a dramatic example of subarctic legacy contamination from mining activities, with remediation costs projected to exceed $1 billion. Operational between 1948 and 2004, gold extraction at Giant Mine released large quantities of arsenic and metals from the roasting of arsenopyrite ore. We examined the long-term ecological effects of roaster emissions on Pocket Lake, a small lake at the edge of the Giant Mine lease boundary, using a spectrum of palaeoenvironmental approaches. A dated sedimentary profile tracked striking increases (approx. 1700%) in arsenic concentrations coeval with the initiation of Giant Mine operations. Large increases in mercury, antimony and lead also occurred. Synchronous changes in biological indicator assemblages from multiple aquatic trophic levels, in both benthic and pelagic habitats, indicate dramatic ecological responses to extreme metal(loid) contamination. At the peak of contamination, all Cladocera, a keystone group of primary consumers, as well as all planktonic diatoms, were functionally lost from the sediment record. No biological recovery has been inferred, despite the fact that the bulk of metal(loid) emissions occurred more than 50 years ago, and the cessation of all ore-roasting activities in Yellowknife in 1999.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".